init files

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import pathlib
+plt = platform.system()
+if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
+learn = load_learner('model.pkl')
+
+
+labels = learn.dls.vocab
+def predict(img):
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+title = "Shirt Neck Classifier"
+description = "Find the shirt neck type"
+article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+examples = ['demo1.jpg', 'demo2.jpg', 'demo3.jpg', 'demo4.jpg', 'demo5.jpg']
+interpretation='default'
+enable_queue=True
+
+gr.Interface(fn=predict,
+             inputs=gr.inputs.Image(shape=(300, 300)),
+             outputs=gr.outputs.Label(num_top_classes=3),
+             title=title,
+             description=description,
+             article=article,
+             examples=examples,
+             interpretation=interpretation,
+             enable_queue=enable_queue).launch()

requirements.txt

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+fastai
+scikit-image